Linked Life Data

19432770

Source:http://linkedlifedata.com/resource/pubmed/id/19432770

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-5-24
pubmed:abstractText
In the genetic study of complex traits, especially behavior related ones, such as smoking and alcoholism, usually several phenotypic measurements are obtained for the description of the complex trait, but no single measurement can quantify fully the complicated characteristics of the symptom because of our lack of understanding of the underlying etiology. If those phenotypes share a common genetic mechanism, rather than studying each individual phenotype separately, it is more advantageous to analyze them jointly as a multivariate trait to enhance the power to identify associated genes. We propose a multilocus association test for the study of multivariate traits. The test is derived from a partially linear tree-based regression model for multiple outcomes. This novel tree-based model provides a formal statistical testing framework for the evaluation of the association between a multivariate outcome and a set of candidate predictors, such as markers within a gene or pathway, while accommodating adjustment for other covariates. Through simulation studies we show that the proposed method has an acceptable type I error rate and improved power over the univariate outcome analysis, which studies each component of the complex trait separately with multiple-comparison adjustment. A candidate gene association study of multiple smoking-related phenotypes is used to demonstrate the application and advantages of this new method. The proposed method is general enough to be used for the assessment of the joint effect of a set of multiple risk factors on a multivariate outcome in other biomedical research settings.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-11108642, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-12925516, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-12925518, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-15116352, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-15180683, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-16138916, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-17135278, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-17227481, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-17266115, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-17401363, http://linkedlifedata.com/resource/pubmed/commentcorrection/19432770-18004205
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
1541-0420
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
66
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
89-96
pubmed:dateRevised
2011-9-28
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A partially linear tree-based regression model for multivariate outcomes.
pubmed:affiliation
Division of Cancer Epidemiology and Genetics, NCI, Rockville, Maryland 20892, USA. yuka@mail.nih.gov
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural, Research Support, N.I.H., Intramural